Explainable Artificial Intelligence (XAI) for Managing Customer Needs in E-Commerce: A Systematic Review

Businesses across industries have changed how they operate as a result of the introduction and adoption of technology. Importantly, significant technological advancements in e-commerce try to persuade consumers to purchase particular goods and brands. AI is increasingly used as a vital new tool for personalization and product customization to meet specific needs. It provides insights into the decision-making criteria, elements, and data required to provide a recommendation. The machine learning field known as XAI studies and strives to understand the models and techniques utilized in the black box decisions produced by AI systems. In order to deploy explainable XAI systems, this study suggested that ML models need to be improved in order to make them easier to comprehend and interpret. A branch of machine learning known as XAI studies and aims to understand the models and processes involved in how AI systems make decisions in a “black box.” It offers insights into the considerations, factors, and information needed to generate a suggestion. This study made the recommendation that ML models be enhanced, making them interpretable and understandable, in order to deploy explainable XAI systems. This paper addresses this issue by examining and analyzing recent work in XAI methodologies, needs, principles, applications, and case studies. We introduce a novel XAI approach that facilitates the development of explainable models while maintaining a high level of learning performance.